From Pilot Project to Scaling

Many companies face the challenge of introducing generative AI (GenAI) without overextending themselves. A structured, iterative approach with clear goals, lean pilot projects and realistic success metrics is crucial. This article shows how companies can initiate, test, evaluate and scale GenAI projects.

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1. Starting Point: Motivations and Areas of Application for GenAI

Before starting any project, the following questions should be answered: What is the pressing problem to be solved (e.g. consulting, data quality, text creation)? Are there already internal ideas regarding customer experience or increased efficiency? What systems and data are available? Typical starting points are high bounce rates for complex products, manual maintenance of extensive product data or recurring support requests on standard topics. These problems are particularly suitable for GenAI, as they can be improved with a manageable amount of effort.

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2. Project Phases at a Glance: From MVP to Go-Live

A tried and tested process comprises four phases:

Phase 1:

Discovery & target definition: identification of stakeholders (e-commerce, IT, marketing, legal), definition of measurable targets (e.g. +30% conversion rate), feasibility analysis (system landscape, data availability).

Phase 2:

Setup & Pilot (MVP): Selection of a specific use case (e.g. Conversational Advisor for a product category), preparation of integration (API, data sources, UI integration), testing and optimisation of content and prompts (incl. editorial guidelines and data protection check).

Phase 3:

Live test & optimisation: carrying out A/B tests with a control group, monitoring relevant KPIs (engagement, uplift, abandonment rate, user feedback), iterative adaptation (UX, prompt templates, data enrichment).

Phase 4:

Rollout & scaling: Expansion to other categories, markets or areas of application, integration into marketing automation, PIM or CRM systems, training and empowerment of teams (training, guidelines, quality assurance processes).

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3. Quick Wins: Rapid Success with GenAI

The following application areas are already showing significant results after a short time:

Conversational Advisor (frontend): Improved advice, higher conversion rates.

Product data maintenance (backend): Reduction of manual work, optimised user experience.

Text creation (content): Accelerated market launch, SEO optimisation.

These use cases can be tested individually or in combination, depending on the resources available and the level of technical maturity.

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4. Success Factors for a Sustainable GenAI Implementation

Successful GenAI projects are characterised by the following aspects:

• Top management support (top-down sponsorship)
• Clear definition of project goals and KPIs (business case)
• Focus on empowering internal teams through user-friendly tools and training (enablement)
• An iterative approach in small steps with frequent testing and fast learning cycles
• Close collaboration with the GenAI provider (vendor partnership)

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5. Lessons learnt from Frontnow Projects

Experience from Frontnow projects shows that the greatest potential often lies not in the front end, but in an improved data structure. GenAI does not replace a strategy, but it can significantly accelerate its implementation. Companies that involve stakeholders from the areas of CX, IT and content at an early stage achieve faster scaling effects. In addition, good prompt guidelines are of great value as they control the quality and brand message.

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Conclusion: Getting Started with GenAI is a First, Well-Considered Step

GenAI does not require a comprehensive transformation, but an intelligent, measurable start. If you select use cases with direct business benefits, test them in a structured manner and optimise them systematically, you will quickly achieve results and gain trust both internally and externally.

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